The Impact of Social Information on Visual JudgmentsJessica Hullman, Eytan Adar, and Priti Shah

Social visualization systems have emerged to support
collective intelligence-driven analysis of a growing influx
of open data. As with many other online systems, social
signals (e.g., forums, polls) are commonly integrated to
drive use. Unfortunately, the same social features that can
provide rapid, high-accuracy analysis are coupled with the
pitfalls of any social system. Through an experiment
involving over 300 subjects, we address how social
information signals (social proof) affect quantitative
judgments in the context of graphical perception. We
identify how unbiased social signals lead to fewer errors
over non-social settings and conversely, how biased signals
lead to more errors. We further reflect on how systematic
bias nullifies certain collective intelligence benefits, and we
provide evidence of the formation of information cascades.
We describe how these findings can be applied to
collaborative visualization systems to produce more
accurate individual interpretations in social contexts.